Clipboard: A Visual Search and Browsing Engine for Tablet and PC

  • David Scott
  • Jinlin Guo
  • Hongyi Wang
  • Yang Yang
  • Frank Hopfgartner
  • Cathal Gurrin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7131)

Abstract

In this work, we present a handheld video browser that utilizes two methods of search; Concept Search and Keyframe Similarity. Concept Search allows a user to define a query using selected visual concepts and presents the user with a cluster of video segments based on extracted image features using OpponentSIFT. Keyframe Similarity has a dependance on the previous search for input criteria, allowing a user to select a keyframe for similarity search, returning three types of results; local keyframes from the current scene, global shot similarity based on visual features and text similarity of shots, based on frequently occurring words generated from ASR transcripts.

Keywords

Multi-modal Access tablet pc visual concept keyframe similarity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating Color Descriptors for Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)CrossRefGoogle Scholar
  2. 2.
    Foley, C., Guo, J., Scott, D., Ferguson, P., Gurrin, C., Smeaton, A.F.: TRECVid 2010 Experiments at Dublin City University. In: TRECVid 2010 - Text REtrieval Conference TRECVid Workshop, Gaithersburg, MD (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • David Scott
    • 1
  • Jinlin Guo
    • 1
  • Hongyi Wang
    • 1
  • Yang Yang
    • 1
  • Frank Hopfgartner
    • 1
  • Cathal Gurrin
    • 1
  1. 1.Dublin City UniversityDublinIreland

Personalised recommendations